{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Populating the interactive namespace from numpy and matplotlib\n"
     ]
    }
   ],
   "source": [
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "import os\n",
    "import sys\n",
    "import io\n",
    "import torch \n",
    "import numpy as np\n",
    "from collections import OrderedDict\n",
    "\n",
    "%pylab inline\n",
    "rcParams[\"figure.figsize\"] = (16,5)\n",
    "sys.path.append('/home/erogol/projects/')\n",
    "\n",
    "import librosa\n",
    "import librosa.display\n",
    "\n",
    "from TTS.models.tacotron import Tacotron \n",
    "from TTS.layers import *\n",
    "from TTS.utils.data import *\n",
    "from TTS.utils.audio import AudioProcessor\n",
    "from TTS.utils.generic_utils import load_config\n",
    "from TTS.utils.text import text_to_sequence\n",
    "\n",
    "import IPython\n",
    "from IPython.display import Audio\n",
    "from utils import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def tts(model, text, CONFIG, use_cuda, ap, figures=True):                                                                \n",
    "    waveform, alignment, spectrogram = create_speech(model, text, CONFIG, use_cuda, ap) \n",
    "    if figures:                                                                                                         \n",
    "        visualize(alignment, spectrogram, CONFIG)                                                                       \n",
    "    IPython.display.display(Audio(waveform, rate=CONFIG.sample_rate))  \n",
    "    return alignment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "ROOT_PATH = '../result/January-26-2018_09:10AM/'\n",
    "MODEL_PATH = ROOT_PATH + '/checkpoint_27600.pth.tar'\n",
    "CONFIG_PATH = ROOT_PATH + '/config.json'\n",
    "OUT_FOLDER = ROOT_PATH + '/test/'\n",
    "CONFIG = load_config(CONFIG_PATH)\n",
    "use_cuda = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# load the model\n",
    "model = Tacotron(CONFIG.embedding_size, CONFIG.hidden_size,\n",
    "        CONFIG.num_mels, CONFIG.num_freq, CONFIG.r)\n",
    "ap = AudioProcessor(CONFIG.sample_rate, CONFIG.num_mels, CONFIG.min_level_db,\n",
    "                    CONFIG.frame_shift_ms, CONFIG.frame_length_ms, CONFIG.preemphasis,\n",
    "                    CONFIG.ref_level_db, CONFIG.num_freq, CONFIG.power, griffin_lim_iters=80)         \n",
    "\n",
    "if use_cuda:\n",
    "    model = torch.nn.DataParallel(model.cuda())\n",
    "else:\n",
    "    cp = torch.load(MODEL_PATH, map_location=lambda storage, loc: storage)\n",
    "    \n",
    "    # remove DataPatallel wrapper\n",
    "    new_state_dict = OrderedDict()\n",
    "    for k, v in cp['model'].items():\n",
    "        name = k[7:] # remove `module.`\n",
    "        new_state_dict[name] = v\n",
    "    cp['model'] = new_state_dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.load_state_dict(cp['model'])\n",
    "# model.decoder.eval(); ##\n",
    "model.encoder.eval();\n",
    "model.postnet.eval();\n",
    "# model.eval();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "sentences = [\n",
    "    \"I try to speak my friend.\",\n",
    "    \"I speak more than binary any more.\",\n",
    "    \"I try ti implement a new TTS system.\"\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# tts(model, sentences[2], CONFIG, use_cuda, ap)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(13099, 3)\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv('/data/shared/KeithIto/LJSpeech-1.0/metadata.csv', delimiter='|')\n",
    "print(df.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([32, 10])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = torch.randn(32, 10)\n",
    "b = torch.nn.functional.softmax(torch.autograd.Variable(a), dim=-1)\n",
    "b.size()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Variable containing:\n",
       " 1.0000\n",
       " 1.0000\n",
       " 1.0000\n",
       " 1.0000\n",
       " 1.0000\n",
       " 1.0000\n",
       " 1.0000\n",
       " 1.0000\n",
       " 1.0000\n",
       " 1.0000\n",
       " 1.0000\n",
       " 1.0000\n",
       " 1.0000\n",
       " 1.0000\n",
       " 1.0000\n",
       " 1.0000\n",
       " 1.0000\n",
       " 1.0000\n",
       " 1.0000\n",
       " 1.0000\n",
       " 1.0000\n",
       " 1.0000\n",
       " 1.0000\n",
       " 1.0000\n",
       " 1.0000\n",
       " 1.0000\n",
       " 1.0000\n",
       " 1.0000\n",
       " 1.0000\n",
       " 1.0000\n",
       " 1.0000\n",
       " 1.0000\n",
       "[torch.FloatTensor of size 32]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b.sum(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([1, 156])\n",
      "torch.Size([1, 156])\n"
     ]
    },
    {
     "data": {
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      "For although the Chinese took impressions from wood blocks engraved in relief for centuries before the woodcutters of the Netherlands, by a similar process\n"
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A7q4quMeY7l5McktWvtSf9k+SfKC775qMP7DGPe9hsIqEqvpnSV6R5LVJ3pbk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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5878081438>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "aling = tts(model, df.iloc[1, 1], CONFIG, use_cuda, ap)\n",
    "print(df.iloc[1, 1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.], dtype=float32)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(aling)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# import IPython\n",
    "# import glob\n",
    "\n",
    "# # wav_files = glob.glob(OUT_FOLDER+'/**/*.wav', recursive=True)\n",
    "# # assert len(wav_files) > 0\n",
    "# # IPython.display.Audio(wav_files[1])\n",
    "# IPython.display.display(IPython.display.Audio(wav, rate=CONFIG.sample_rate))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}